Confounding can cause substantial bias in nonexperimental studies that aim to estimate
causal effects. Propensity score methods allow researchers to reduce bias from measured …

Backgrounds. Clinicians need guidance to address the heterogeneity of treatment
responses of patients with major depressive disorder (MDD). While prediction schemes …

There has been considerable interest across several fields in methods that reduce the
problem of learning good treatment assignment policies to the problem of accurate policy …

This book builds on and is a sequel to our book Targeted Learning: Causal Inference for
Observational and Experimental Studies (2011). Since the publication of this first book on …

Missing data affect nearly every discipline by complicating the statistical analysis of collected
data. But since the 1990s, there have been important developments in the statistical …

Estimating causal effects under exogeneity hinges on two key assumptions:
unconfoundedness and overlap. Researchers often argue that unconfoundedness is more …

Methodological advancements, including propensity score methods, have resulted in
improved unbiased estimation of treatment effects from observational data. Traditionally, a …

Background & Aims There is an urgent need for safe treatments for irritable bowel syndrome
(IBS) that relieve treatment-refractory symptoms and their societal and economic burden …

The consistency of propensity score (PS) estimators relies on correct specification of the PS
model. The PS is frequently estimated using main-effects logistic regression. However, the …

Targeted maximum likelihood estimation (TMLE) is a general approach for constructing an
efficient double-robust semi-parametric substitution estimator of a causal effect parameter or …